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🗣 Wd提问: 商品搜索引擎

🤖 Ai回答:
商品搜索引擎系统设计

1、系统架构概览

核心组件
数据采集层：爬虫、API接入
数据处理层：数据清洗、特征提取
存储层：倒排索引、向量数据库
搜索引擎层：查询解析、排序算法
服务层：API接口、缓存机制

2、核心功能模块

2.1 数据采集与处理
python
class ProductDataProcessor:
def __init__(self):
self.features = []

def extract_features(self, product_data):
提取商品特征
features = {
'title': self.extract_title(product_data),
'description': self.extract_description(product_data),
'price': self.extract_price(product_data),
'category': self.extract_category(product_data),
'brand': self.extract_brand(product_data),
'tags': self.extract_tags(product_data)
}
return features

def normalize_text(self, text):
文本标准化处理
去除特殊字符、转小写、分词等
pass

2.2 倒排索引构建
python
class InvertedIndex:
def __init__(self):
self.index = {} term [doc_ids]
self.doc_vectors = {} doc_id feature_vector

def build_index(self, products):
构建倒排索引
for product in products:
doc_id = product['id']
terms = self.tokenize_product(product)

for term in terms:
if term not in self.index:
self.index[term] = []
self.index[term].append(doc_id)

构建文档向量
self.doc_vectors[doc_id] = self.create_vector(product)

def search(self, query_terms):
执行搜索
计算TF-IDF权重
执行布尔检索或向量相似度计算
pass

2.3 搜索算法实现
python
class SearchEngine:
def __init__(self):
self.inverted_index = InvertedIndex()
self.vector_db = VectorDatabase()

def search_products(self, query, filters=None, sort_by='relevance'):
主搜索方法
1、查询解析
parsed_query = self.parse_query(query)

2、多维度搜索
semantic_results = self.semantic_search(parsed_query)
keyword_results = self.keyword_search(parsed_query)

3、结果融合
combined_results = self.combine_results(
semantic_results, keyword_results
)

4、过滤和排序
filtered_results = self.apply_filters(combined_results, filters)
sorted_results = self.sort_results(filtered_results, sort_by)

return sorted_results[:20] 返回前20个结果

def semantic_search(self, query):
语义搜索
使用BERT等模型进行语义匹配
pass

def keyword_search(self, query):
关键词搜索
基于倒排索引的关键词匹配
pass

3、关键技术实现

3.1 向量搜索优化
python
class VectorSearchEngine:
def __init__(self):
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
self.faiss_index = None

def build_vector_index(self, products):
构建向量索引
embeddings = []
product_ids = []

for product in products:
text = self.get_product_text(product)
embedding = self.embedding_model.encode(text)
embeddings.append(embedding)
product_ids.append(product['id'])

使用FAISS构建近似最近邻搜索
self.faiss_index = faiss.IndexFlatIP(len(embeddings[0]))
self.faiss_index.add(np.array(embeddings).astype('float32'))
self.product_ids = product_ids

def search_similar(self, query, k=10):
相似商品搜索
query_embedding = self.embedding_model.encode(query)
scores, indices = self.faiss_index.search(
np.array([query_embedding]).astype('float32'), k
)

results = []
for i, score in zip(indices[0], scores[0]):
results.append({
'product_id': self.product_ids[i],
'similarity_score': score
})
return results

3.2 实时推荐系统
python
class RecommendationEngine:
def __init__(self):
self.user_profiles = {}
self.item_similarity = {}

def get_recommendations(self, user_id, product_id=None, limit=10):
获取推荐结果
if user_id not in self.user_profiles:
return self.get_popular_items(limit)

user_profile = self.user_profiles[user_id]

协同过滤推荐
collaborative_recs = self.collaborative_filtering(user_profile)

内容推荐
content_recs = self.content_based_recommendation(product_id)

混合推荐
hybrid_recs = self.hybrid_recommendation(
collaborative_recs, content_recs
)

return hybrid_recs[:limit]

def collaborative_filtering(self, user_profile):
协同过滤
基于用户行为的相似性分析
pass

def content_based_recommendation(self, product_id):
基于内容的推荐
基于商品特征的相似性计算
pass

4、性能优化策略

4.1 缓存机制
python
import redis
import json

class CacheManager:
def __init__(self):
self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
self.cache_ttl = 3600 1小时

def cache_search_result(self, query, results):
缓存搜索结果
key = fsearch:{hash(query)}
self.redis_client.setex(
key,
self.cache_ttl,
json.dumps(results)
)

def get_cached_result(self, query):
获取缓存结果
key = fsearch:{hash(query)}
cached = self.redis_client.get(key)
if cached:
return json.loads(cached)
return None

4.2 分布式部署
yaml
docker-compose.yml
version: '3'
services:
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:7.17.0
ports:
9200:9200
environment:
discovery.type=single-node

redis:
image: redis:alpine
ports:
6379:6379

search-engine:
build: .
ports:
8000:8000
depends_on:
elasticsearch
redis

5、用户体验优化

5.1 搜索建议
python
class SearchSuggestion:
def __init__(self):
self.suggestions = {}

def generate_suggestions(self, query):
生成搜索建议
基于历史搜索、热门搜索、拼写纠错等
suggestions = []

1、热门搜索词
hot_words = self.get_hot_words(query)
suggestions.extend(hot_words)

2、拼写纠错
corrected = self.spell_correct(query)
if corrected != query:
suggestions.append(corrected)

3、相关搜索词
related = self.get_related_terms(query)
suggestions.extend(related)

return list(set(suggestions))[:5]

5.2 智能排序
python
class SmartRanker:
def __init__(self):
self.weights = {
'relevance': 0.4,
'popularity': 0.3,
'price': 0.2,
'rating': 0.1
}

def rank_products(self, products, query):
智能排序
ranked = []

for product in products:
score = (
self.calculate_relevance_score(product, query) * self.weights['relevance'] +
self.calculate_popularity_score(product) * self.weights['popularity'] +
self.calculate_price_score(product) * self.weights['price'] +
self.calculate_rating_score(product) * self.weights['rating']
)

ranked.append((product, score))

return sorted(ranked, key=lambda x: x[1], reverse=True)

6、监控与维护

6.1 性能监控
python
import time
from functools import wraps

def monitor_performance(func):
@wraps(func)
def wrapper(*args, kwargs):
start_time = time.time()
result = func(*args, kwargs)
end_time = time.time()

记录性能指标
print(f{func.__name__} took {end_time start_time:.4f}s)
return result
return wrapper

class PerformanceMonitor:

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